Interpretable Machine Learning Platform for Genomic Target Identification in Drug Discovery

Overview

This solution offers a proprietary suite of machine learning algorithms that identify complex patterns in spectroscopic and genomic data to accelerate drug discovery. Unlike conventional black-box models such as neural networks, the approach is fully interpretable, revealing which specific portions of the input data are most related to the targeted outcome. This transparency makes it possible to pinpoint genetic markers associated with disease, supporting target identification for new therapeutics.

The platform is designed to support pharmaceutical and biotech partners in areas such as neuropsychiatric drug discovery, where it can help identify genetic correlates of disease, build classification and regression models for clinical measurements, monitor disease progression, and assess patient fitness for clinical trials. Its interpretability also makes it well suited for regulated and data-driven decision-making environments.

Technical specifications

Key features:

• Interpretable machine learning algorithms that identify complex patterns in spectroscopic and genomic data
• Transparent models that reveal which genetic inputs are most predictive of disease outcomes, rather than obscuring them within opaque neural network layers
• Capability to control for population variations and covariance that may confound outcome predictions
• Originally developed for pharmaceutical process analytical technologies, where it reduced model-building time from days to minutes
• Successfully correlated clinical chemistry data with COVID outcomes with higher accuracy than published benchmarks
• Applicable to both classification (diseased vs. non-diseased) and regression (continuous clinical measurements) modeling tasks
• Extensible to additional data types including lipodomics and other biological systems

Technology readiness level

The technology has progressed through multiple validation stages. It was initially developed and validated for pharmaceutical analyte identification from crude reaction mixtures using process analytical technologies, where it demonstrated significant reductions in model development time and expertise requirements. It has since been validated on biological systems, including a successful correlation of clinical chemistry data with COVID outcomes that exceeded published accuracy benchmarks. Early-stage application to genomic data has yielded promising confidential results, and the team is actively seeking additional datasets to further develop and refine the models for genomic and lipodomic applications. The platform is ready for collaborative pilot projects with pharmaceutical partners to expand validation in neuropsychiatric and other disease areas.